RECEIVE JAYWING'S RISK INSIGHTS
Identifying hidden fraud networks: Why fraud detection needs a network-based approach
Fraud is now networked. Learn how graph databases help detect fraud rings, reduce losses and improve real-time decision making.
Sample size and model choice: When GBMs outperform DNNs in credit risk
When do GBMs outperform DNNs in credit risk modelling? New research shows how sample size and number of defaults influence machine learning model performance.
Smarter fraud and AML convergence: Escaping the silos
Why fraud and AML separation weakens detection and what unified, graph-based architecture requires by 2026.
Geopolitical shocks and credit risk: Are your models ready?
How geopolitical realignment challenges credit risk forecasting. Lessons from climate risk modelling and the 2025 BCST for UK banks and risk teams.
Machine learning model stability: Do Gradient Boosting Machines (GBMs) and Deep Neural Networks (DNNs) really degrade faster?
Machine learning models often outperform early, but what happens after go-live? We look at long-term performance of GBMs and DNNs using multi-year credit data.
Tackling telecom-enabled fraud through smarter data collaboration
UK fraud losses topped £629m in H1 2025. Real-time telecom intelligence now lets banks intervene during scams. What CROs need to know.